Why is balancing exploration and exploitation crucial in reinforcement learning?
- To optimize the learning process
- To simplify the problem
- To minimize the rewards
- To increase computational efficiency
Balancing exploration and exploitation is crucial because it helps the agent learn the environment without getting stuck in suboptimal actions.
Which layer in a CNN is responsible for reducing the spatial dimensions of the input data?
- Convolutional Layer
- Pooling Layer
- Fully Connected Layer
- Activation Layer
The Pooling Layer is responsible for spatial dimension reduction. It downsamples the feature maps, reducing the amount of computation needed and retaining important information.
Gaussian Mixture Models (GMMs) are an extension of k-means clustering, but instead of assigning each data point to a single cluster, GMMs allow data points to belong to multiple clusters based on what?
- Data Point's Distance to Origin
- Probability Distribution
- Data Point's Neighbors
- Random Assignment
GMMs allow data points to belong to multiple clusters based on probability distributions, modeling uncertainty about cluster assignments.
In Policy Gradient Methods, the policy is usually parameterized by ________ and the gradient is taken with respect to these parameters.
- Neural Networks
- Q-values
- State-Action Pairs
- Rewards
In Policy Gradient Methods, the policy is often parameterized by neural networks. These networks determine the probability distribution of actions.
Policy Gradient Methods often use which of the following to estimate the gradient of the expected reward with respect to the policy parameters?
- Monte Carlo estimation
- Finite difference
- Gradient ascent
- Random sampling
Policy Gradient Methods often use Monte Carlo estimation to estimate the gradient of the expected reward with respect to policy parameters. It involves sampling trajectories and averaging returns to estimate the gradient.
While t-SNE is excellent for visualization, it can sometimes produce misleading results due to which of its properties?
- Crowding Problem
- Curse of Dimensionality
- Convergence Issues
- Data Scaling
t-SNE can produce misleading results due to the "Curse of Dimensionality," which can lead to points appearing too clustered together in high-dimensional space, making it challenging to visualize and interpret.
In a video game with multiple levels and complex interactions, what approach is suitable for training an AI agent optimally?
- Transfer Learning
- Curriculum Learning
- Random Search
- Supervised Learning
Curriculum Learning is ideal for training an AI agent to handle various levels with different challenges. It starts with easy levels, gradually increasing difficulty based on the agent's performance in earlier stages, ensuring effective learning.
The ability of an individual or a group to understand and trust the model's decisions is often tied to the model's ________.
- Explainability
- Complexity
- Accuracy
- Processing speed
Model explainability is essential for understanding and trusting a model's decisions, especially in critical applications like healthcare or finance, where transparency is key for decision-making and accountability.
Which machine learning algorithm is commonly used for time series forecasting due to its ability to remember long sequences?
- Decision Trees.
- Recurrent Neural Networks (RNNs).
- Support Vector Machines (SVMs).
- K-Means Clustering.
Recurrent Neural Networks (RNNs) are favored for time series forecasting because they can remember and model long sequences of data, making them suitable for sequential data like time series.
Random Forests introduce randomness in two main ways: by bootstrapping the data and by selecting a random subset of ______ for every split.
- Data Points
- Features
- Leaves
- Trees
Random Forests introduce randomness by selecting a random subset of "Features" for every split in each tree. This helps in creating diverse trees, which collectively improve the overall performance and reduce the risk of overfitting.